dailyai.report

The 2016 paper Zhang et al. challenged classical deep learning theory by demonstrating that models can memorize random labels. This finding shattered optimism regarding traditional generalization bounds. It forced researchers to acknowledge that standard theoretical frameworks fail to explain why neural networks actually work. Practitioners must now rely on empirical evidence over legacy theory.